Dissecting genomic regions and underlying sheath blight resistance traits in rice ( Oryza sativa L.) using a genome‐wide association study

Abstract The productivity of rice is greatly affected by the infection of the plant pathogenic fungus Rhizoctonia solani, which causes a significant grain yield reduction globally. There exist a limited number of rice accessions that are available to develop sheath blight resistance (ShB). Our objective was to identify a good source of the ShB resistance, understand the heritability, and trait interactions, and identify the genomic regions for ShB resistance traits by genome‐wide association studies (GWAS). In the present study, a set of 330 traditional landraces and improved rice varieties were evaluated for ShB resistance and created a core panel of 192 accessions used in the GWAS. This panel provides a more considerable amount of genetic variance and found a significant phenotypic variation among the panel of rice accessions for all the agro‐morphological and disease‐resistance traits over the seasons. The infection rate of ShB and disease reaction were calculated as percent disease index (PDI) and area under the disease progress curve (AUDPC). The correlation analysis showed a significant positive association between PDIs and AUPDC and a negative association between PDI and plant height, flag leaf length, and grain yield. The panel was genotyped with 133 SSR microsatellite markers, resulting in a genome coverage of 314.83 Mb, and the average distance between markers is 2.53 Mb. By employing GLM and MLM (Q + K) models, 30 marker–trait associations (MTAs) were identified with targeted traits over the seasons. Among these QTLs, eight were found to be novel and located on 2, 4, 8, 10, and 12 chromosomes, which explained the phenotypic variation ranging from 5% to 15%. With the GWAS approach, six candidate genes were identified. Os05t0566400, Os08t0155900, and Os09t0567300 were found to be associated with defense mechanisms against ShB. These findings provided insights into the novel donors of IC283139, IC 277248, Sivappuchithirai Kar, and Bowalia. The promising genomic regions on 10 of 12 chromosomes associated with ShB would be useful in developing rice varieties with durable disease resistance.

GWAS.This panel provides a more considerable amount of genetic variance and found a significant phenotypic variation among the panel of rice accessions for all the agro-morphological and disease-resistance traits over the seasons.The infection rate of ShB and disease reaction were calculated as percent disease index (PDI) and area under the disease progress curve (AUDPC).The correlation analysis showed a significant positive association between PDIs and AUPDC and a negative association between PDI and plant height, flag leaf length, and grain yield.The panel was genotyped with 133 SSR microsatellite markers, resulting in a genome coverage of 314.83 Mb, and the average distance between markers is 2.53 Mb.By employing GLM and MLM (Q + K) models, 30 marker-trait associations (MTAs) were identified with targeted traits over the seasons.Among these QTLs, eight were found to be novel and located on 2, 4, 8, 10, and 12 chromosomes, which explained the phenotypic variation ranging from 5% to 15%.With the GWAS approach, six candidate The lack of established resistant sources against R. solani instigate rice farmers to manage the disease by adapting cultural practices and chemical fungicides (Goswami et al., 2019).Besides, the excessive use of chemical fungicides remains in the soil and increases the chances of an unfriendly environment and several health issues.Moreover, there is a chance of developing resistance to pathogens against the continuous use of fungicides.The development of resistant rice genotypes against sheath blight disease needs to be improved due to the unsuccessful in the identification of stable resistant donors (Yadav et al., 2015).The high genetic variability of R. solani, a broad range of host compatibility, and the ability to survive across seasons by forming sclerotia that remain dormant in the soil make it difficult to manage the pathogen (Molla et al., 2020).
They identified 11 SNP loci that were significantly associated with sheath blight resistance.Besides, Zhang et al. (2019) experimented on sheath blight resistance using 2,977,750 SNP loci in 563 rice accessions.They detected 132, 562, and 75 SNP loci associated with lesion height, relative lesion height (RLH), and culm length, respectively.However, stable QTLs identified across seasons/locations from multiple mapping populations would be desirable to use in the crop improvement program.To date, only two QTLs (qSBR11-1 and qSb-11 LE ) resistance to sheath blight were fine mapped (Channamallikarjuna et al., 2010;Zuo et al., 2013).Earlier sheath blight resistance mapping studies typically relied on lesion length to differentiate the population.
In the present investigation, we have collected 330 diverse sets of rice genotypes, including both traditional landraces and improved rice varieties.They were evaluated against ShB disease at Banaras Hindu University (BHU), Varanasi, India.To explore further, a panel consisting of 192 selected genotypes was formed to represent the initial population.The specific goal of this study was to (i) examine the phenotypic, genotypic variances and relationship between agro-morphological and ShB disease-associated parameters over the season, (ii) understand the molecular diversity pattern, population structure, and relationship between the assumed subpopulation of the genotypes of core panel based on the SSRs, and (iii) identify the genomic regions that are associated with ShB tolerance traits and further to reveals the potential candidate genes and their mechanism.

| Experiment 1
In the present investigation, the summary of descriptive statistics and distribution pattern of data observed related to sheath blight traits of 330 genotypes are presented in Table 1.Among the different sheath blight-related traits studied, 7th, 14th, 21st, and 28th day RLH and 7th and 14th day percent disease index (PDI) were negatively skewed (<À1), whereas other traits were positively skewed.Similarly, kurtosis values were positive for most of the traits, such as plant height and panicle length, 14th and 21st day PDI, mean PDI and 14th day PDI were >3, while 7th day PDI was found to be negative.It clearly showed the distribution of the population was leptokurtic, which indicates that germplasm was not more closely bunched around the mean.
The coefficient of variation (CV) was considerable, and it was higher for RLH 7th day (64%) followed by 28th (52%) and 21st (49%) day PDI, while the least CV was found in 28th day RLH (16%).Higher CV indicated that the population in this study exhibited a higher variability.The mean, median, and mode were similar in all traits other than plant height, 7th day, and 14th day RLH, which indicates distribution frequency was normal for these traits (Figure 1).
The hierarchical cluster analysis was performed across 330 genotypes to find their similarity based on the disease reaction against sheath blight (Figure 2).They were classified into three major clusters.

| Experiment 2
Population distribution of the core panel suggests that the population captured a wide range of variation over the seasons (dry and wet season 2019) (Table S3).The mean and median were similar over the seasons, which suggested that population frequency was normal.
Among the traits studied in dry season 2019, plant height was negatively skewed (<À1), while other traits were positively skewed (Table S4).In wet season 2019, all the traits were positively skewed except flag leaf width, and ligule shape exhibited negative skewness (Table S5).Similarly, kurtosis values were ranged from À1 to +1 for all the traits over the seasons, other than days to 50% flowering (2.30)  S6).
The principal component analysis was performed to categorize genotypes based on their disease reaction and to find the association between the traits responsible for sheath blight resistance.The biplot analysis of season 1 (dry season) registered 41.33% of the variation by PC1 and 14.82% by PC2 (Figure S1).Among the different traits studied, AUDPC, and PDI of different time intervals contributed to a high level of variation compared to other traits, while morphological traits were plotted in opposite quadrant.On the other hand, a similar trend of variation was observed among the traits studied during season 2 (wet season) with a cumulative variability of 38.87% by PC1 and PC2 (Figure S2).The cultivar-by-trait biplot of season 1 categorizes 192 genotypes into three groups such as MR, MS, and S. whereas the biplot of season 2 categorizes them into HS, MR, MS, and S.However, the biplot analysis of pooled data depicted a similar trend of association between sheath blight disease-related traits with 38.56% of variation toward the total variability (Figure 3).
The Pearson correlation analysis was performed to find out the estimated correlation coefficient among the traits associated with traits responsible for sheath blight disease of rice for two seasons.During dry season 2019, a strong positive correlation was found between 21st day PDI and 14th day PDI (0.746), mean PDI (0.919), and AUPDC (0.841).
However, a negative association was observed between plant height and disease-related traits such as PDI and AUPDC (Figure S3).In the 2019 wet season, a similar trend of the strong positive association was observed among PDI of 14th, 21st, 28th, mean, and AUPDC (Figure S4).The estimated correlation coefficient of traits over the season concerned with sheath blight resistance reflected a similar trend of positive association between PDI at all levels of weekly interval and AUPDC.A negative association between PDI and plant height, flag leaf length, and grain yield was also observed (Figure 4).

| Genetic diversity
In the present investigation, a shortlisted panel population of 192 rice genotypes of different provinces of India was studied to realize their genetic diversity using 133 sheath blight linked and general SSRs to decipher their genetic relatedness.The output of genetic kinship and diversity parameters are depicted in Table S7.The total number of alleles observed were 366 with 2.781 average alleles per locus.The allele number were differed from one (RM8217, RM188, RM435, RM5304) to seven (RM426) per loci.The major allele frequency values were ranged from 0.239 (RM426) to 1.000 (RM8217, RM188, RM435, RM5304) with an average of 0.642 per marker.Further, the polymorphic information content (PIC) value varied from 0.000 (RM8217, RM188, RM435, RM5304) to 0.794 (RM426) with the mean value of 0.383 per loci.The highest gene diversity observed was 0.819 for the primer RM426, while the least value of 0.000 was observed for three primers (RM8217, RM188, RM435, and RM5304).
Heterozygosity values of 133 markers were found to be low and ranged from 0.0000 to 0.142 with an average of 0.013.The mean inbreeding coefficient value of 133 loci was 0.972 and ranged from 0.126 to 1.00 (Table S7).With the progression of days from 7 to 28 days, RLH, and PDI increase.The upper, median, and lower quartiles of boxes represent the 75th, 50th, and 25th percentiles of the population, respectively.The square box inside the quartile box represents mean, and asterix represents outliers.

| Genetic relatedness by cluster analysis
Cluster analysis was performed to determine the genetic distance and dissimilarity matrix using the unweighted neighbor-joining (UNJ) method.The Unrooted tree classified genotypes into three major clusters (Figure 5).Cluster 1 (red) consisted of 107 genotypes and further subdivided into clusters 1a and 1b with 82 and 25 genotypes, respectively.Similarly, cluster 2 (blue) consists of 79 genotypes and subdivided into cluster 2a with 63 genotypes and 2b with 16 genotypes.
Cluster 3 (green) was the smallest and consisted of six genotypes (Figure 5a).The output of the UNJ tree was also marked in different shades based on their population structure result (Figure 5b), disease reaction (Figure 5c) and eco-geographical origin (Figure 5d) to appreciate their genetic relatedness.
2.5 | Analysis of molecular variance (AMOVA), Nei's genetic diversity, and principal coordinate analysis (PCoA) To determine the genetic differentiation among the core population utilized for the present study, the 192 genotypes were further showed that maximum variation (87%) was found among the individuals, followed by between the populations (11%) and within individuals (2%) (Figure 6 and Table 3).The deviation from Hardy-Weinberg's prediction was calculated using Wright's F statistics.The F IS and F IT values for all the loci were 0.979 and 0.981, while F ST was found to be 0.110 between the populations (Table 3).Similarly, the NM value of the assumed population was 2.02.To determine the Nei genetic diversity among the assumed subpopulation, a scatter plot was constructed using principal coordinate analysis (PCoA).The PCoA explained that the first two components accounted for 80.67% of the total genetic variation among the assumed population (Figure 7).
Moderately resistant and moderately susceptible genotypes were distributed in all four quadrants, while most of the susceptible genotypes were grouped in quadrant 1.The highest pairwise Nei genetic distance was noticed between population 1 and population 2 (0.216), followed by population 1 with population 3 (0.193) and 4 (0.144).

| Population structure analysis
The population structure of 192 genotypes was determined using a model-based approach program Structure 2.3.4.The peak plateau of ad hoc measure ΔK was found to be K = 2 with ΔK value of 438 (Figure 8a).The model-based approach classified the genotypes into two subpopulations (SP1 and SP2), and ancestry threshold of >70% was considered as pure, while <70% considered as admixtures (Figure 8b).Among the 192 genotypes studied, 70 genotypes were placed in SP1, 107 genotypes fitted in SP2, and 15 genotypes were grouped as an admixture.SP1 comprised 29 MR, 32 MS, and nine susceptible genotypes with a population proportion of 36.45% (Table S8).Similarly, 35 moderately resistant, 69 moderately susceptible, and three susceptible genotypes were fitted in SP2 with 55.72% of the population proportion.There was 7.81% membership proportion in admixtures with seven MR and eight MS genotypes.
The fixation index (Fst) values of the two populations were 0.1939 and 0.2823 for SP1 and SP2, respectively.Maximum allele frequency divergence among the populations was obtained in SP1 and SP2 (0.0827) based on net nucleotide distance computed using point estimates of P. The average distance (expected heterozygosity) among the individuals in the panel population was 0.2914 and 0.2764 for SP1 and SP2, respectively.

| LD mapping
The genetic association analysis of 133 SSRs and sheath blight related and other morphological traits of 192 genotypes was performed using a generalized linear model (GLM) and mixed linear model (MLM/Q + K) model with the program TASSEL version 5.2.63 (Table S9).The association between the SSRs and traits such as PDI of 7, 14, 21, and  respectively (Table 4).Further, both GLM and MLM (Q + K) models showed a total of 30 SSRs were associated across the different seasons of experiments.Among the 30 associations, 23 SSRs were associated with sheath blight related traits, whereas seven SSRs had an association with plant height (5) and culm color (2).Considering both GLM and MLM (Q + K), nine SSRs, namely.RM3482, RM250, RM1216, RM13, RM16, RM178, RM306, RM334, and RM5428, had a highly significant association with sheath blight disease-related traits such as PDI of 7, 14, 21, and 28 days, mean PDI, and AUDPC across the three conditions (season 1, season 2, and pooled data) (Figure 9).
Markers RM81 and RM3823 were found to be associated with basal leaf sheath color QTLs as detected by both GLM and MLM models with >5.0% phenotypic variability on chromosomes 3 and 9, respectively.
The association of other morphological traits such as panicle length, days to 50% flowering, tiller number, grain yield, internodal length, ligule color, auricle color, apiculus color, culm thickness, flag leaf width, and flag leaf length are presented in Table S9.
F I G U R E 3 PCA biplot graph represents genotypes in two main principal components for traits associated with sheath blight disease over the season.The two components explained 27.02% and 11.54% of the variance, respectively.The direction and length of the vector indicate the traits' contribution to the first two components in the PCA.The transparency of the trait vectors represents the contribution to the variance in the dataset, ranging from 3% (lightest) to 12% (darkest).
2.8 | Candidate genes underlying QTLs for traits related to sheath blight disease over the season Twenty-three different disease-related QTLs and seven plant height and basal leaf sheath color QTLs were considered for the identification of candidate genes (Table 5).For the trait plant height, five candidate genes were identified and located on chromosomes 2, 5, 6, 8, and 12. Besides, two genes with locus id Os03t0122300 and Os09t0567300 were identified for basal leaf sheath color, while the rest of the candidate genes were identified for sheath blightassociated traits.Among the candidate genes identified related to sheath blight, two gene (Os01t0629900 and Os05t0572000) were associated with all six sheath blight-associated traits, namely, PDI of 7, 14, 21, and 28 DAI, MP, and AUDPC, located on chromosomes 1 and 5, respectively, whereas Os03t0600600 and Os05t0246600 were identified to be associated with five sheath blight related traits and presented on chromosomes 3 and 5, respectively.The network analysis of the above candidate genes in the QTL interval region was analyzed using the rice FRIEND database, and it explored the co-expression pattern of the genes.A total of six candidate genes were taken forward to construct gene network, and their interactions The output of the UNJ tree was utilized to depict the output of population structure analysis by using different colors to understand the similarity of the grouping of genotypes.Genotypes were colored as per the structure analysis, 70 genotypes were categorized as subpopulation 1 (blue color), and 107 as subpopulation 2 (red color), and 15 were considered as admixtures (green color).(c) Additionally, the phylogenetic tree was also utilized to appreciate the nature of disease reaction between and within clusters obtained from the field data using different colors.Moderately resistant (MR) genotypes were coded in red, moderately susceptible (MS) genotypes were in the green, while susceptible (S) genotypes were marked in purple.Genotypes within cluster 1, 49 MR, 49 MS, and nine susceptible genotypes were grouped.Similarly, cluster 2 contained 20 MR, 56 MS, and three susceptible, whereas two MR genotypes and four MS genotypes were grouped in cluster 3. (d) On the other hand, these clusters were marked as per their geographical origin of the genotypes to find the association between the eco-geographical distribution of genotypes and genetic divergence.The figure 5d highlighted red as AUS, NRRI improved varieties in green, Tamil Nadu (TN) improved varieties and landraces in pink, and Uttar Pradesh (UP) improved varieties and landraces in blue.
were displayed in Figure S5.Further, the loci information and functions of genes associated with the candidate genes were shown in Table S10.The sum of nodes and edges of co-expressed genes of the six candidate genes was 36 and 38, respectively.Among the six putative genes, the loci Os08g0155900 located on chromosome 8 had functionally associated with the locus Os11g0702100 (similar to class III chitinase homologue OsChib3Hh).
2.9 | Identification of rice germplasm with QTLs related to sheath blight traits, culm color, and plant height Nei genetic diversity among the assumed subpopulation using principal coordinate analyses (PCoA).The assumed four subpopulations were plotted separately across three quadrants.Indicated the panel population is found to be diverse.

| DISCUSSION
Cultivation of genetically similar rice accessions over a large scale enforces the high selection pressure on the pathogen populations, which results in cultivars becoming highly vulnerable to biotic stresses.Further, climate change and the emergence of new virulent races enable a continuous threat to rice production as well the world's food security (Yadav et al., 2019).Thus, chemical protection measure constrains sustainable progress to keep up the pace with the evolving pathogens (Vasudevan et al., 2016).The genetic potential of conserved accessions (landraces, cultivated varieties, and wild accessions) needs to be fully investigated for the identification of new resistance genes/alleles to impart genetic tolerance in varieties.In the present investigation, we performed genetic diversity and evaluated geographically diverse rice germplasm that is unique, unexplored, and untapped against sheath blight disease of rice grown in seven major states of India and Bangladesh.
Our study found that out of 192 rice accessions studied, none of the variety found immune (complete resistance) to sheath blight, whereas 18 accessions were found to be moderately resistant (MR),  6).The accession IC 283139 showed very less disease incidence relative to other germplasm studied over the three seasons.Similarly, we observed the similar level of tolerance in IC 283139 across six environments (Panda et al., 2023).Dey et al. (2016) also reported that a few genotypes, namely, 10-3 (introgression line), SM801 (N22 mutant), Wazuhophek, Ngnololasha Phougak, and Gumdhan (Gall midge biotype differential), were moderately resistant to sheath blight disease based on 3-year field screening analysis.
In our repeated testing of genotypes over the season, the previously identified moderately resistant check varieties such as Teqing (Li et al., 1995;Pinson et al., 2005;Yin et al., 2009) and Jasmine 85 (Liu et al., 2009;Pan et al., 1999;Zuo et al., 2013) were found to be moderately susceptible to sheath blight pathogen.Furthermore, our finding was in agreement with Dey et al. (2016), who has reported that Jasmine 85 showed moderately susceptible reaction to R. F I G U R E 1 1 LD decay (r 2 ) curve plotted against the distance between pairs of loci on chromosomes in rice.
T A B L E 5 Colocalization of significant markers and candidate genes believed to be involved in sheath blight-related traits, plant height, and culm color identified in the panel population.reaction to R. solani.These findings suggest that pathotype, cultivar, and environmental interaction determines the resistance reaction (Naveenkumar et al., 2022).

| Genetic diversity
The genetic construction of diverse rice accessions can be analyzed by using distance-based and model-based clustering approach (Ngangkham et al., 2018;Pradhan et al., 2020;Yadav et al., 2017).To  and Anandan et al. (2022).Further, heterozygosity was found to be low with an average of 0.013 per loci, and average inbreeding coefficient value per primer was 0.972, which is in agreement with previous reports on rice (Anandan et al., 2016;Nachimuthu et al., 2015).Based on SSR genotypic data, a distance-based neighbor-joining (NJ) tree categorized the core population of 192 genotypes into three major clusters.Among the three clusters, the MR accessions were grouped in cluster 1, while moderately susceptible genotypes were categorized in cluster 2. Our finding is in agreement with Yadav et al. (2019) who reported that the majority of resistant and susceptible genotypes were grouped into distinct clusters screened against the disease rice leaf blast.Concurrently, Susan et al. (2019) et al., 2016;Jia et al., 2012;Pradhan et al., 2020) with varied level of the threshold between 55% and 80% to determine the genotype relatedness to a specific subpopulation (SP).In the present study, SP1 consists of 70 accessions, followed by 107 belonging to SP2, and 15 were categorized as an admixture.The AMOVA estimates the molecular variance within the individuals in the panel population.In our study, the highest percentage of the proportion of variation was found among the individuals (87%), the lowest variant existed between the individuals (11%), and the least was observed (2%) within the individuals.There is enough number of reports in agreement with our observation for biotic stress response in rice (Yadav et al., 2017(Yadav et al., , 2019) ) and abiotic stress (Ngangkham et al., 2018;Pradhan et al., 2020).The genetic variability estimated by the fixation index revealed that 0.11 (Fst) indicates the existence of moderate genetic differentiation within the population and inbreeding coefficient F IS and F IT values were found to be 0.979 and 0.981, respectively.Conversely, Anandan et al. (2016) reported little divergence existed between rice subpopulation (Fst = 0.016).The moderate level of genetic differentiation in the present study might be due to the collection of large number of landraces from different provinces of India and Bangladesh.

| Marker and rice sheath blight resistance trait associations
Association mapping becomes the most effective tool in mapping the genes of interest by utilization of ex situ conserved natural genetic diversity of crop germplasm resources.With regard to the QTL identification for sheath blight resistance, earlier researchers utilized the RLH as sheath blight trait for QTL identification in biparental and association mapping panel (Liu et al., 2014;Taguchi-Shiobara et al., 2013;Wen et al., 2015;Yadav et al., 2015).Among them, very few stable QTLs were reported across location/seasons (Han et al., 2003;Pinson et al., 2005;Zuo et al., 2013), unlikely no reports available on QTL cloned for R. solani resistance in rice.Therefore, in the present investigation, we have used the PDI of different time intervals such as 7th, 14th, 21st, and 28th DAI of R. solani for QTL identification.Further, AUDPC generated using this different day's interval was also used to detect the sheath blight responsive QTLs.To our best knowledge, this is the first report for sheath blight QTL identification in rice using PDI and AUDPC traits to detect the QTLs associated with sheath blight resistance in rice.
Added to the above, leucine-rich repeat gene was also found located at 0.35 Mb right of the QTL qShB.9-1 on chromosome 9 associated with 14th and 21st day PDI with PV of 5% and 7% in GLM and MLM, respectively.Furthermore, this QTL was found to be overlapped with the QTLs (qSBR9 and qSBR9.1)reported by Channamallikarjuna et al. (2010) and Yadav et al. (2015).The QTLs qShB.3-6 and qShB.6-1 showed a stronger association with PDI on the 21st day with a PV of 8% and 6% in GLM and 6% and 7% in MLM analysis.The resistance imparted by these QTLs might be due to the locus Os03t0860100 being at 0.05 Mb right of qShB.3-6 on chromosome 3 and Os06t0667900 at 0.38 Mb right of qShB.6-1 on chromosome 6 responsible for positive regulation of disease resistance (Table 5).
The novel sheath blight disease-resistant QTL (qShB.10-1,qShB.10-2)related to mean PDI on chromosome 10 was observed to have PV of 7%-14% and 8%-10% in both GLM and MLM analysis in response to RM5392 and RM8015, respectively (  2010) also reported the association of marker loci RM1216, RM3482, and RM306 with sheath blight resistance and plant height on chromosome 1.Higher PV was found with these markers to be the stronger association with the nearest candidate genes associated with these QTLs, that is, leucine-rich repeat domain-containing protein at 0.67 Mb on the right side of the locus RM1216, pathogenesis-related transcriptional factor and ERF domain-containing protein at 0.57 Mb on right side of the locus RM3482, and blast and wound-induced mitogen-activated protein kinase associated with the locus RM306 at 0.71 Mb on the left side of the QTL (Table 5).
The QTLs qShB.5-1 and qShB.5-3registered PV of 7%-14% (GLM) and 5%-10% (MLM) for PDI and AUDPC was found associated with SSR genomic region of RM13 and RM334 on chromosome 5.The nearest candidate genes such as leucine-rich repeat domaincontaining protein (0.68 Mb away left side from the marker RM13) and pathogenesis-related transcriptional factor and ERF domaincontaining protein (0.05 Mb away right angle from the locus RM334) may involve in the sheath blight disease resistance (Table 5).Previously, Liu et al. (2009) also detected these marker loci on chromosome 5 contributing to sheath blight disease resistance with 5.4% PV.
Knowing the effect of favorable alleles on the traits of interest is one of the crucial parameters to strengthen the performance of modern rice cultivars by introgressing through MAS (Ngangkham et al., 2018).In the present experiment, elite alleles imparting resistance against sheath blight disease in rice were identified (Table S12) and that would be useful in developing rice varieties with durable disease resistance.Interestingly, the following 11 elite alleles associated with markers, namely, RM250 (Chr 2), RM1350 (Chr 3), RM16 (Chr 3), RM3117 (Chr 3), RM335 (Chr 4), RM13 (Chr 5), RM178 (Chr 5), RM5784 (Chr 5), RM400 (Chr 6), RM310 (Chr 8), and RM5428 (Chr 8), were found to enhance the disease resistance for sheath blight tolerance (Figure 9).Besides, several researchers (Che et al., 2003;Liu et al., 2009;Sharma et al., 2009;Taguchi-Shiobara et al., 2013;Yadav et al., 2015) have observed the above said markers to have association with biotic resistance in rice.The network analysis of the locus Os08g0155900 by the rice FREND database revealed that the coexpression pattern of the genes was highly expressed in class III chitinase, which is responsible for disease resistance.Similarly, the marker RM5428 (chromosome 8) from this study suggested that by controlling traits of sheath blight resistance, also the QTLs were associated with the candidate gene OsDR10 and function of this gene is pathogen-induced defense responsive protein.This report on identification of elite alleles and their candidate genes for disease tolerance would be the first report for sheath blight disease, and it might lead researchers to identify reliable resistance sources having elite alleles for the development of durable resistance.

| CONCLUSION
Assessing large number of rice germplasm for resistance against sheath blight disease brings valuable information.We conclude from our experiment that sheath blight disease resistance should be studied on 21st and 28th days' post inoculation of R. solani.The germplasm utilized to study diversity patterns by subjecting trait-linked microsatellite markers has classified tolerant and susceptible genotypes.The result of dissimilarity analyses and distance-based approach was in corroboration with model-based structure analysis.In the present study, we identified 30 QTLs that had a highly significant association with sheath blight disease resistance.Among them, eight QTLs, namely, qPh.2-1, qShB.4-1,qShB.4-2,qPh.8-1, qShB.8-2,qShB.10-1,qShB.10-2, and qPh.12-1, were novel QTLs related to sheath blight resistance.Notably, 13 LRR genes on eight chromosomes (1, 2, 3, 4, 5, 8, 9, and 12) were found to have associated with sheath blight disease resistance in rice that would be useful in developing varieties with durable sheath blight disease resistance in rice.However, these QTLs need to be further validated before they could be utilized for marker-assisted breeding in rice.The newly identified donors from this study possessing favorable alleles could be used for imparting resistance against sheath blight disease in rice after functionally characterizing them.

| Planting materials
A total of 330 diverse rice (Oryza sativa L.) accessions encompassing cultivated varieties and landraces of indica and aus (Table S1) were collected from the Gene Bank, ICAR-National Rice Research Institute (NRRI), Cuttack, Odisha; Department of Genetics and Plant Breeding, Banaras Hindu University (BHU), Varanasi, Uttar Pradesh; and Department of Rice, Tamil Nadu Agricultural University, Coimbatore.
The agro-morphological trait and source of seeds are presented in Table S1.

| Phenotyping experiment 1
The 330 diverse rice genotypes were evaluated for sheath blight resis-

| Phenotyping experiment 2
The core population developed from experiment 1 was evaluated in infuse disease in the plants (Goswami et al., 2019;Panda et al., 2023).
Several phenotypic parameters related to sheath blight such as plant height (cm), number of tillers per plant, days to 50% flowering, panicle length (cm), flag leaf width (cm), flag leaf length (cm), internodal length (cm), culm thickness (mm), basal leaf sheath color, ligule color, auricle color, apiculate color, and grain yield were observed at appropriate stages following IRRI SES score.Besides, disease severity was monitored at weekly intervals such as 7, 14, 21, and 28 DAI of the pathogen in each genotype.The RLH was estimated using the following formula described by Sharma and Teng (1990).
RLH ¼ maximum height at which lesion appear=plant height ð Þ Â 100 Disease scoring (0-9 scale) given by IRRI (2013) was used for the disease assessment, and the PDI was estimated as follows Baker and Wheeler (1970).
PDI ¼ sum of all rating= total number of observations Â maximum rating scale ð Þ ð Þ Â 100 The AUDPC was calculated by using the corresponding disease severity percentage of each disease score taken four times at 7 days' interval.AUDPC was generated by using the formula given by Shaner (1977).
where X i is the disease index expressed as a proportion at the i th observation, t i is the time (days after planting) at the i th observations, and n is the total number of observations.tation (Murray & Thompson, 1980).DNA quality and concentration were estimated by using 0.8% agarose gel electrophoresis.Further, sample DNA was diluted to 30 ng/μL.For the present study, 103 sheath blight-linked microsatellite markers from previously published reports of biparental mapping and 30 SSR primers were selected in such a way that it has distributed across all the rice chromosomes to illustrate the diversity (Table S2).

| PCR amplification and visualization of markers linked to sheath blight resistance
The PCR amplification was performed in a thermocycler (Bio-Rad) using 10 μl of PCR mixture, which contained 15 ng of genomic DNA,

F
I G U R E 1 Box plot showing the population distribution for sheath blight-related traits of 330 rice genotypes.(a) Plant height and days to 50% flowering.(b) RLH of 7th and 14th day, PDI of 7th and 14th day.(c) RLH of 21st and 28th day, PDI of 21st day, 28th day, and PDI mean.

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I G U R E 2 Hierarchical clustering of 330 rice genotypes based on morphological variation among the population screened against sheath blight disease.Three hundred and thirty genotypes grouped into three major clusters.Cluster 1 had five genotypes, cluster 2 had four genotypes, and cluster 3 subdivided into two sub-clusters 3a and 3b.Sub-cluster 3a (green and red color) contains 10 genotypes representing MR and R genotypes, and sub-cluster 3b contains 71 genotypes subdivided again into 3b-1 (green) and 3b-2 (subdivided into two sub-clusters 3b-2A (brown; 199 genotypes represents MR and R) and 3b-2B (purple; 41 genotypes represents MR and MS)).subdivided into four groups (NRRI improved varieties [11], Tamil Nadu [24], Uttar Pradesh [32], and AUS [125]) based on their ecogeographical origin.The analysis of molecular variance (AMOVA) 28 days after inoculation (DAI), mean PDI, AUDPC, plant height, and culm color was performed for each season (dry season 2019 (season 1) and wet season 2019 (season 2)) and pooled phenotypic data for the association analysis.A total of 8, 14, and 19 SSRs were found to be associated with season 1, season 2, and pooled over the season, T A B L E 2 Analysis of variance (ANOVA) for agronomic and sheath blight related traits in rice genotypes over the season.

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I G U R E 4 Pearson correlation matrix among the traits associated with sheath blight disease over the season.The color denotes the sort of correlation, where 1 represents complete positive correlation (dark green) and À1 represents complete negative correlation (dark brown) between two traits.Large circle denotes strong, and small circle denotes weaker correlation.F I G U R E 5 Unrooted tree using unweighted neighbor-joining (UNJ) method depicting clustering pattern of core population 192 germplasm lines in response to 133 SSRs.(a) Darwin clustered genotypes into three major groups.Cluster 1 (red) comprised 107 genotypes, further subgrouped into clusters 1a and 1b with 82 and 25 genotypes, respectively.Cluster 2 (blue) consists of 79 genotypes and subgrouped into cluster 2a (63 genotypes) and 2b(16 genotypes).Cluster 3 (green) consist of six genotypes.(b)
135 were moderately susceptible, and 39 showed a susceptible reaction.Similarly,Goswami et al. (2019) reported that 10 accessions were F I G U R E 9 Molecular genetic map on the basis of rice chromosomes on the linkage disequilibrium study, which includes QTLs responsible for sheath blight resistance and other agronomic traits.(Thirty marker loci were associated with sheath blight disease-related traits and agronomic traits such as plant height and basal leaf sheath color.Markers associated with sheath blight related traits were highlighted using different colors on the basis of number of traits associated.Marker highlighted in dark green associated with six traits, blue five, orange four, light pink three, sky blue two; red, single trait; light green, agronomic traits such as plant height; and dark pink, basal leaf sheath color.*Represents novel QTLs identified in this study.)found to be promising and showed resistance against sheath blight disease out of 261 genotypes evaluated based on PDI and AUDPC values.On the other hand, among the 18 MR genotypes identified, only 11 showed less disease incidence in comparison to tolerant check variety, Tetep (Table solani, and Bal et al. (2020), who reported that Teqing showed susceptible F I G U R E 1 0 Quantile-quantile (QQ) plot and distribution of SSR markers and traits association.14P, PDI of 14th day; 21P, PDI of 21st day; 28P, PDI of 28th day; 7P, PDI of 7th day; AC, auricle color; AP, apiculus color; AUDPC, area under disease progress curve; BC, basal leaf sheath color; DH, days to 50% flowering; FL, flag leaf length; FW, flag leaf width; GY, grain yield; IL, internodal length; LC, ligule color; LS, ligule shape; MP, mean PDI; NT, tiller number; PH, plant height; PL, panicle length; ST, culm thickness.
Abbreviations: 14PDI, PDI of 14th day; 21PDI, PDI of 21st day; 28PDI, PDI of 28th day; 7PDI, PDI of 7th day; AUDPC, area under disease progress curve; BC, basal leaf sheath color; MP, mean PDI; PH, plant height; locus name based on Rice Annotation Project Database (RAPDB); QTL location, distance of the loci from the marker.
our knowledge, our work might be the first report from India and the second in the globe, wherein sheath blight trait-linked SSRs were utilized to evaluate the population structure and genetic diversity in 192 germplasm lines of Indian rice accessions.Further, a complete analysis of sheath blight trait-linked microsatellite markers in a diverse collection of rice would be helpful for breeders in designing breeding strategies for sheath blight disease resistance.In the present investigation, 133 SSR loci generated 366 alleles with a mean value of 2.781 and a mean PIC value of about 0.383.The comparison of our findings with earlier reports in rice suggested similar observations with population panel-based variations.For example,Yadav et al. (2019) found that the average number of the allele was 1.76 with 0.25 mean PIC value of Indian rice landrace collection.Similarly,Pradhan et al. (2020) also observed average alleles per locus was 2.44, with one to six alleles per loci, and PIC values range from 0.000 to 0.671 with the mean of 0.355 in the collection of rice accessions including landraces and improved varieties.The average major allele frequency value was found to be 0.642, whereas the gene diversity value was 0.443 per locus.This is in agreement with previous reports of Anandan et al. (2016), Yadav et al. (2019), Pradhan et al. (2020), Chettri et al. (2021), T A B L E 6 List of genotypes identified with their QTL details and disease reaction against sheath blight in rice.
use of trait-linked SSRs in the present study might be the reason for the smaller number of subgroups identified by the population structure analysis, but they were very well differentiated based on sheath blight disease reactions.Similarly, Yadav et al. (2017) and Susan et al. (2019) reported that the STRUCTURE and PCoA scatter plot clearly grouped the panel population into resistant and susceptible subpopulations evaluated against rice leaf blast disease.
tance during the wet season (Kharif ) 2018 at the experimental farm, BHU (28.18 0 N, 38.03 0 E, and 75.5 masl), Varanasi.The experiment was laid out in a 156 m 2 plot following an augmented block design with 13 blocks.Two seedlings per hill were planted with a spacing of 20 and 15 cm between and within rows, respectively.The moderately resistant (Tetep, Jasmine 85, and Teqing) and susceptible checks (Pusa Basmati-1 and Tapaswini) were planted in each block in replicated mode with 25 test entries per block.The recommended dose of fertilizers such as 120:60:60 kg NPK ha À1 was applied as basal and top dressing.The highly virulent strain of R. solani (MTCC12227) was obtained from the Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India.During tillering stage of the crop, from the top second leaf sheath was placed with 4-day-old immature sclerotia of R. solani (AG1-IA) grown on potato dextrose agar at 26 ± 2 C and tied with wet cotton to provide moisture for the initial pathogen development(Goswami et al., 2019;Panda et al., 2023).Based on the phenotypic assessment of sheath blight, we have developed a core population set consisting of 192 rice genotypes representing the diversity of the whole population that includes genotypes of S, MS, MR, and R and genotypes from each cluster (Figure2) following the normal distribution used for association mapping studies.The core panel includes the cultivated varieties and landraces with known tolerant (Tetep, Jasmine 85, Teqing) and susceptible checks (Pusa Basmati 1 and Tapaswini) of sheath blight.
the experimental plot of NRRI (20 o 27 0 09 00 N, 85 o 55 0 57 00 E, 26 masl), Cuttack, during the dry (Rabi) and wet (Kharif) seasons of 2019.Initially, the seeds were heat-treated to break the seed dormancy by keeping them in the hot-air oven for 45 h at 50 C. Seedlings were raised in an elevated nursery bed and 30 days after sowing, and the healthy seedlings were carefully uprooted from nursery beds and transplanted into the main field along with the checks.The experiment was laid out in a 175 m 2 area in a randomized block design with three replicates.Two seedlings per hill were planted in a 1.5 m row length of 20 cm between rows and 15 cm between plants.The recommended dose of fertilizers was applied as a basal and top dressing (120:60:60 kg NPK ha À1 ).At the tillering stage of the crop, immature sclerotia of the R. solani (AG1-IA) (MK478903.1)was inoculated to

5. 4 |
Genotyping of the core panel 5.4.1 | DNA isolation and quantification Young leaf samples were collected from 21-day-old seedlings of 192 genotypes of the panel population.Total genomic DNA of all the genotypes was isolated using an automated tissue homogenizer (1600 MiniG ® , NJ) in CTAB extraction buffer (100 mM Tris-HCl pH 8, 20 mM EDTA pH 8, 1.3 M NaCl, 2% CTAB) and chloroform-isoamyl alcohol extraction followed by RNAse treatment and ethanol precipi-